Recently, representation learning of trajectories has drawn lots of attention because it requires very little feature engineering and similarity computation, since using representations naturally avoids the problem of point matching. Studies mostly related to our work include trajectory clustering [1]} and trajectory similarity computation [2]}, [3]}, [4]} via representation learning. The authors in [1]} use a seq2seq autoencoder to learn trajectory representations for clustering tasks. They first extract trajectory features such as speed and rate of turn and transform them into a feature sequence that describes the movement pattern of the corresponding object. Then, they feed the feature sequences into their model to learn fixed-length representations. Later the work in [2]} proposes an RNN based model to learn trajectory representations for similarity computation. The model does not require feature extraction and directly learns from original trajectories represented by trajectory tokens. However, the model training is supervised and requires to construct training pairs by sampling from the original trajectories. Both studies in [1]}, [2]} focus on finding trajectories with similar
shapes, regardless of their actual timestamps. Other studies like [4]} propose to inject additional semantic information such as environmental constraints and trajectory activities into deep models, in order to obtain more accurate trajectory representations for similarity computation. Nonetheless, none of the existing models is designed to learn simultaneously spatial and temporal proximity between trajectories. As such they cannot be directly applied on discovering traveling companions that are both spatially and temporally close.
